Principal, Machine Learning Software Engineer, Admin

Admin Central is building the future of how administrators work across Workday, turning a fragmented patchwork of tools and disconnected AI into a single, unified experience. Within Admin Central, Workday implementations move from being driven by human effort to being powered by intelligence designed into the platform.

You’ll build mission-critical distributed systems, high-availability usage pipelines, new agentic functionality, and event-driven data flows across global product regions to ensure enterprise billing integrity at scale.

If you're excited to build from scratch, move fast, and transform how thousands of admins work every day, come join us!

As a Principal Software Engineer (ML/AI) on the Admin Central team, you will lead the architecture and implementation of Workday’s next-generation AI platforms and autonomous agent frameworks.

You’ll sit at the intersection of machine learning, event-driven data systems, and enterprise platform engineering—transforming cutting‑edge AI research into production-ready, highly reliable systems handling tens of millions of daily requests for hundreds of enterprise tenants.

In this hands‑on, high‑impact role, you will design the ML infrastructure, multi‑agent orchestration layers, and high‑availability usage pipelines that power enterprise‑grade decision‑making, usage tracking, and automatic tenant maintenance.

• Agentic AI & Orchestration:
  Design, implement, and maintain advanced agentic capabilities, including tool calling, multi‑agent workflows, long‑range planning, reasoning frameworks, and human‑in‑the‑loop integrations.
• Scalable ML Infrastructure:
  Build secure, high‑throughput REST services and event‑driven data pipelines using Python, AWS, and cloud‑native tech. Architect multi‑tenant runtimes that deliver fast, low‑latency inference to millions of global users.
• MLOps & Enterprise Reliability:
  Establish end‑to‑end MLOps standards—including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy/anonymization guardrails—ensuring systems are explainable, secure, and production‑resilient.
• Technical & Strategic Leadership:
  Partner closely with software engineers, Product Managers, Data Engineering, SRE, and Legal/Compliance stakeholders to turn ambiguous business needs into scalable technical designs and long‑term data‑governance strategies.

• 12+ years in ML development and R&D leadership, including 8+ years with advanced ML methodologies, distributed frameworks, and cloud-based platforms — designing complex, scalable, production‑grade ML systems.
• 6+ years building and hosting scalable ML services, microservices, and high‑throughput data pipelines in production using Java, Go, Scala, or Python.
• 4+ years working with LLMs, generative AI, RAG pipelines, and autonomous agent frameworks for real‑world enterprise applications.

• Data Processing & Pipelines: Expert in high‑throughput streaming (Kafka), real‑time feature extraction (Spark/Flink), and distributed columnar storage (Click House, Iceberg) to architect large‑scale ML pipelines.
• AI/ML Expertise: Deep knowledge of deep learning, LLM orchestration, RAG, and agentic systems — applying ML algorithms and Model‑Based Design (including MCP integrations and tool‑use frameworks) to multi‑agent coordination, intent classification, and workflow optimization.
• Data Analysis & Feature Engineering: Proven ability to lead EDA on multi‑tenant agentic workflows to shape data strategy, paired with mastery of high‑dimensional embeddings, vector database management, and data isolation.
• Model Lifecycle: Proven ability to lead model building, fine‑tuning, and deployment of domain‑specific…